Key Takeaways

• EverMind stores memory as local Markdown files backed by SQLite and LanceDB, requiring no MongoDB, Elasticsearch, or Redis dependencies.

• EverMind scores 93.05% on LoCoMo, 83.00% on LongMemEval, and 90.04% recall on HaluMem across peer-reviewed benchmarks.

• Both vendors publish memory evaluations, but the reported metrics and setups differ: EverMind reports LongMemEval accuracy, while Supermemory reports LongMemEval-S Recall@20 and other results. Those percentages are not directly interchangeable.

• EverMind is licensed under Apache 2.0, permitting commercial use and redistribution without software royalties while reducing code-level lock-in.

• Both platforms offer free hosted entry tiers and MCP access through their supported services. EverOS is open source; Supermemory's self-hosted server is a separate, non-open-source binary with a lite license limit. EverOS and Supermemory's local option have different storage models; neither is literally free to operate once compute and model costs are included.

• EverMind's published LoCoMo, LongMemEval, HaluMem, and latency figures are more relevant to this memory-layer comparison than a model benchmark unrelated to agent-memory retrieval. Compare both products on the same recall tasks before ranking them.

• EverMind suits multi-agent and private-deployment teams needing auditable memory; Supermemory suits developers preferring a managed API with minimal infrastructure ownership.

At a Glance

EverMind vs Supermemory: The Verdict at a Glance

Choose EverMind when editable Markdown files and a portable self-hosted memory layer are your priority. Choose Supermemory when its managed API, connectors, or documented local graph-based deployment better fits your stack. Both publish retrieval evidence, but not in directly comparable formats.

EverMind publishes this comparison on evermind.ai, its own product site — that first-party position is stated plainly so readers weigh the analysis accordingly.

EverMind fits teams that need memory files they can inspect and move. Supermemory fits developers who want either a managed memory API or its documented local deployment, which is not an open-source server. Compare their benchmark methods and operational requirements, not just a headline score.

Both offer hosted plans and self-hosting. Supermemory MCP is available on its platform, not in the local binary. EverOS uses Markdown with SQLite and LanceDB indexes; Supermemory documents an embedded local graph and local embeddings for self-hosting. Its cloud and local paths should be evaluated separately.

At-a-Glance Head-to-Head Comparison Table

EverMind and Supermemory differ across licensing, storage, and pricing. The table below maps these differences side by side.

What Is EverMind and What Is Supermemory?

EverMind and Supermemory take different approaches to agent memory. EverMind is a self-evolving, portable AI-agent memory operating system, while Supermemory offers a managed memory platform and a separately licensed local server for agents and RAG applications.

EverMind (EverOS + Raven) Explained

EverOS manages memory storage, retrieval, and lifecycle with editable Markdown records and local indexes. Its Run→Case→Skill workflow develops reusable agent skills. EverMind's published memory benchmarks relate to this system; memory results from separate setups do not establish a head-to-head winner.

EverMind stores every memory as a local Markdown file, backed by SQLite and LanceDB. No MongoDB, Elasticsearch, or Redis instance is required. That architecture means a developer inspects, diffs, or migrates memory files with standard text tooling — no proprietary export step.

EverOS is Apache 2.0 licensed and can be self-hosted without a software-license fee. EverMind reports results on LoCoMo, LongMemEval, and HaluMem for its own setup. Its Skill Hub and Run→Case→Skill workflow give teams a route from agent experience to reusable skills.

Supermemory Explained

Supermemory offers a managed API and connectors, alongside a public repository of MIT-licensed SDK/components. Its self-hosting guide describes a separate local server binary with an embedded graph engine and local embeddings, plus a local model option. The server source is not public, and the lite license has limits. That is a real deployment alternative to its hosted service.

Architecture: Memory Layer, Retrieval, RAG & Knowledge Graph Approaches

EverOS stores editable Markdown files backed by SQLite and LanceDB. Supermemory's hosted service abstracts storage, while its local setup uses an embedded graph and local embeddings. The distinction is not local versus cloud; it is file-native memory versus a different graph-based store and service model.

EverMind's storage stack runs entirely on local infrastructure. SQLite holds structured metadata and relational indexes. LanceDB serves as the vector store for semantic recall. No MongoDB, Elasticsearch, or Redis dependency exists in the stack.

Every memory artifact is a plain Markdown file that operators read, edit, version-control, and diff with standard tooling. The lookup process is therefore auditable: a developer traces exactly which file, which passage, and which embedding distance produced a given recall result.

EverMind's pipeline also implements a self-evolving loop called Run→Case→Skill. A Run is a single agent execution. EverMind promotes high-value Runs to reusable Cases, then distills repeated Cases into durable Skills.

EverMind's Run→Case→Skill workflow can turn useful agent experience into reusable skills. Check the current Skill Hub for its catalog rather than treating an unsourced built-in count as a deployment guarantee.

Supermemory offers two paths: a hosted API and a documented self-hosted setup with an embedded graph and local embeddings. The hosted path minimizes operations; the local path gives teams more control, though its records are not EverOS-style editable Markdown files.

EverMind and Supermemory diverge across 3 structural contrasts worth naming explicitly:

• Storage substrate: Markdown with SQLite and LanceDB indexes (EverOS) versus an embedded graph with local embeddings in Supermemory's self-hosted path

• Inspection: direct Markdown-file edits (EverOS) versus Supermemory's API and graph-oriented local storage

• Memory lifecycle: EverMind's Run→Case→Skill workflow versus Supermemory's own ingestion and retrieval model

Some teams require full inspection of every memory record at the file system level. Others operate in air-gapped environments. Both find EverMind's Markdown-native design directly compatible with those constraints.

Self-Hosting & Open Source: Supermemory's GitHub Angle vs EverMind's Model

Self-hosting options separate EverMind and Supermemory more than their different licensing models suggest. Both support running on your own infrastructure, and Supermemory's local server is not open source. Their dependency profiles differ as well.

Supermemory publishes MIT-licensed SDK/components separately from its local server. Its self-hosting guide describes the server's embedded graph engine, local embeddings, and optional local model. Its hosted API remains the simpler route for teams that do not want to run infrastructure. The local storage path does not require an external managed search service; model and ingestion settings still determine other network calls.

EverMind is released under the Apache 2.0 license. The storage stack runs on local Markdown, SQLite, and LanceDB. No MongoDB, Elasticsearch, or Redis dependency is required. That dependency profile means an EverMind deployment runs on a single machine without coordinating multiple external services.

EverOS, EverMind's on-premises distribution, targets private enterprise environments where memory records must remain inside a corporate network. It is a direct fit for teams integrating with private CRM, ERP, or data-warehouse platforms.

There are 4 honest trade-offs worth naming between the two models:

• Supermemory's managed API removes infrastructure burden for teams that prefer metered consumption over server administration.

• EverMind's file-native storage (Markdown + SQLite) gives operators direct, inspectable access to every memory record at the file system level.

• Supermemory's self-hosted path requires its documented local graph setup; compare that operational work with EverOS's Markdown, SQLite, and LanceDB stack rather than assuming managed dependencies.

• EverMind's Apache 2.0 license permits commercial use, modification, and redistribution without royalty obligations.

Teams that prioritize portable memory files and full control over their hosting environment find EverMind's model the more operationally self-contained choice. Teams that need a metered memory API with managed connectors and minimal infrastructure ownership find Supermemory's cloud offering the lower-friction starting point.

API, MCP & SDK: Integration Surface for AI Agents

Both products expose API and MCP integration paths. EverMind emphasizes reusable agent memory across tools; Supermemory provides SDK and platform MCP routes, plus a local API route that does not include Supermemory MCP. Test the exact Claude Code, Codex, or Cursor workflow your team needs.

EverMind's integration model centers on the Raven harness, a memory-first execution layer. Raven attaches persistent, cross-system memory to any agent stack. The framework itself need not manage memory state. Raven exposes memory retrieval and write operations through a single surface.

Claude Code, Codex, and Cursor agents draw on the same memory layer regardless of which underlying system or orchestrator is active. Cross-agent portability is a design property of EverMind, not a configuration option: memory written by one agent is immediately readable by another running on a different system.

EverMind supports the Model Context Protocol (MCP), giving any MCP-compatible tool direct access to its retrieval and storage operations without custom middleware. The SDK covers the languages most common in development for these systems. The Apache 2.0 license removes any restriction on embedding the tool in proprietary pipelines.

Supermemory publishes REST, SDK, and MCP integration paths. Developers can use its managed endpoint or assess the local deployment; it is not restricted to a remote API-only architecture.

EverMind and Supermemory diverge on 2 structural differences that determine which fits a given stack. There are 2 differences that shape the choice for teams building agent pipelines:

• Agent integration: EverMind's memory workflow connects through its documented API and MCP surfaces; Supermemory offers a local API, while its MCP route is platform-only

• Cross-tool memory: both can serve more than one client, but setup and data ownership differ between EverOS files and Supermemory's hosted or local store

Teams building multi-agent, multi-model pipelines on Claude Code, Codex, or Cursor find EverMind's Raven harness the lower-friction path to persistent, portable memory.

Retrieval Quality & Benchmarks: Accuracy, Latency, and Verifiable Evidence

Both companies publish memory results. EverMind reports LoCoMo, LongMemEval, and HaluMem figures; Supermemory publishes research results including LongMemEval-S Recall@20. They use different metrics and configurations, so neither set settles a head-to-head accuracy question.

EverMind reports 93.05% on LoCoMo, 83.00% on LongMemEval, and 90.04% recall on HaluMem for its published setup. These are first-party results; their tasks and scoring should be checked before comparison with another vendor's research page.

EverMind's query response sits at <500ms p95 query time, meaning 95% of production queries resolve in under half a second. Token efficiency reaches approximately 10× lower cost relative to naive full-context approaches, with the observed range spanning 7–15× depending on workload density.

EverMind publishes its own memory benchmark results on its product page, but those results are not a controlled head-to-head test against Supermemory (source).

Supermemory publishes memory research, including a 97% LongMemEval-S Recall@20 result and other benchmark claims. Recall@20 measures whether an answer-bearing item appears in the top 20; it is not the same metric as EverMind's 83.00% LongMemEval answer accuracy. Test both on the same questions for an apples-to-apples decision.

For production teams, the useful evidence is workload-specific: answer accuracy, retrieval recall, latency, and cost under the same conditions. EverMind's named results and Supermemory's research both provide starting points, but neither vendor's figures alone prove superiority here.

Pricing & Free Tiers Face-Off

EverOS self-hosting has no software-license fee under Apache 2.0. Supermemory local is a non-open-source binary that is free within its lite license limit; the MIT public repository does not license that server. Both also offer hosted plans; Supermemory meters cloud usage through monthly credits and per-operation rates. Infrastructure, model-provider, and support costs still matter for self-hosted teams.

EverOS Cloud currently lists Free at $0/month and Pro at $25/month. Supermemory lists Free at $0/month, Pro at $19/month, Max at $100/month, and Scale at $399/month. The plans use different credits and quotas, so list prices alone do not establish a workload-level cost winner.

Supermemory's model suits teams that prefer a fully managed, metered service and accept variable usage costs in exchange for zero infrastructure responsibility. EverMind suits teams that prioritize portable memory files and control over their hosting environment, where the dominant cost is engineering time rather than spend on queries.

The practical cost comparison depends on ingestion volume, retrieval queries, hosting, and model-provider choices. Both vendors offer a free hosted entry tier and a self-hosted path, but Supermemory local has a separate lite license limit; compare their current pricing pages against your expected workload.

Ideal Use Cases: Production Agents, Personal Memory & RAG Apps

EverMind suits teams that want portable Markdown memory and a self-hosted path. Supermemory suits teams that prefer its managed API, connectors, or graph-based local deployment. Both have published memory evidence, though their reported metrics differ.

There are 4 primary scenarios to map across the 2 tools. They are covered in the sections below:

Production AI Agents

EverOS can run its Markdown and index stack on infrastructure a team controls. Supermemory offers both a hosted API and a local graph-based setup. Production teams should compare retrieval results and operational needs under the deployment they would actually run.

Personal Memory and Digital Twin

Personal memory applications demand long-horizon recall across unstructured conversations. Both EverMind and Supermemory address this scenario. EverMind's EverMe module is purpose-built for it, drawing on benchmark results that demonstrate strong long-context accuracy on LoCoMo and LongMemEval evaluations. Supermemory's connector-first design suits personal productivity integrations where information already lives in third-party services.

RAG Applications

RAG pipelines benefit from low-latency, high-precision lookups. EverMind and Supermemory serve them differently. EverMind delivers competitive latency at the p95 level and carries peer-reviewed accuracy evidence spanning 3 public benchmarks. Supermemory provides a metered API that RAG developers can call without managing retrieval infrastructure themselves.

Multi-Agent and Enterprise Private Deployment

EverOS's Apache 2.0 license and editable files suit teams that prioritize portable records. Supermemory's separately licensed local option may also help with data-sovereignty requirements; its managed service is a separate choice for teams comfortable with hosted storage.

Who Should Choose EverMind

EverMind is the right pick for developers building multi-agent or switching-agent systems. It also suits teams that require auditable self-hosted memory under an open-source license, and enterprises that need private on-premises deployment with CRM or ERP integration.

There are 3 primary reader profiles for EverMind:

1. Multi-Agent and Switching-Agent Developers

EverMind's memory layer persists context across agent handoffs. This makes it the correct architecture for workflows where Claude Code, Cursor, or custom agents pass control between one another. The portable Markdown + SQLite + LanceDB storage stack means memory files move with the agent, not with the host.

2. Teams Requiring Auditable, Self-Hosted Open-Source Memory

EverMind ships under the Apache 2.0 license with no MongoDB, Elasticsearch, or Redis dependency. Teams prioritizing portable memory files and control over their hosting environment deploy EverMind on-premises and audit every retrieval path directly in the codebase.

3. Enterprises Needing Private Deployment with CRM/ERP Connectivity

EverOS can run in a private environment, with model-provider and integration endpoints configured to meet the team's data policy. Enterprise CRM or ERP connections require implementation; they should not be assumed to exist as prebuilt integrations.

If a team wants managed memory with minimal operations, both vendors offer cloud paths. Supermemory may be attractive for its connectors and API; EverOS Cloud is an option for teams that also value Markdown portability.

Who Should Choose Supermemory

Developers who want a metered memory API with managed connectors and no intention of operating their own infrastructure should choose Supermemory.

Supermemory publishes MIT-licensed SDK and component code on GitHub, but the self-hosted server binary is not open source. Teams needing server-source inspection should account for that difference before choosing either deployment. The public SDK code can be inspected, but it does not provide source-level visibility into the local server. Teams already comfortable with hosted, cloud-resident storage — and willing to accept usage-based billing in exchange for zero operational overhead — fit Supermemory's design assumptions directly.

Supermemory is also a reasonable fit for builders prototyping personal-memory applications or lightweight RAG pipelines. These builders want connectors to external services wired up quickly, without writing integration code from scratch.

There are 3 profiles Supermemory serves well:

• Developers who prioritize a managed API over self-hosted control

• Teams assessing local deployment who can work within the server's lite license and source-visibility limits

• Prototype builders who need pre-built connectors and accept cloud-resident memory storage

Supermemory may not fit teams that require memories as directly editable Markdown files. It does, however, publish benchmark material and offers a local deployment; evaluate its retrieval quality and residency under the configuration you would use.

How to Migrate or Switch Between EverMind and Supermemory

Switching away from EverMind is largely a file-copy operation, since EverMind stores every memory as a portable, human-readable Markdown file. No vendor tooling is required.

Migrating between EverMind and Supermemory involves 4 practical steps:

1. Export your memories

EverMind memories live as plain Markdown files on disk. Copy the directory to any destination — no export tool or API call is required. Supermemory export capabilities depend on the platform's current data-portability features.

2. Verify structure before import

EverMind Markdown files carry metadata in YAML front-matter. Review that front-matter so the receiving platform can parse timestamps, tags, and source references correctly.

3. Import into the target system

Import the exported content through the destination's documented write API, mapping fields before ingestion. For Supermemory, confirm the current API and local-deployment instructions for the chosen plan rather than assuming Markdown front-matter maps automatically.

4. Validate retrieval after migration

Migration validation means running a representative set of queries against the new setup immediately after import. Confirm that entity references and temporal context resolve correctly before decommissioning the source instance.

Portable content: memory text and mapped metadata can move between systems when export and import paths are available; tags and timestamps should be checked field by field.

Rebuild at the destination: embeddings and indexes are system-specific, so re-index the imported corpus and compare retrieval results before switching agents over.

EverOS's Apache 2.0 license and local Markdown files reduce storage lock-in. A move to another system still requires field mapping, index rebuilding, and retrieval validation.

How We Compared EverMind and Supermemory

We compared current product pages, repositories, pricing, self-hosting guides, and published research for EverMind and Supermemory. This is a feature and evidence comparison, not a claim that both systems were run through the same hands-on benchmark.

The comparison covers storage, deployment, API and MCP support, published retrieval evidence, pricing, and use-case fit. EverMind's figures and Supermemory's research use different metrics, so a shared test would still be needed for a direct performance ranking.

EverMind publishes this article and reports its own benchmark results. Supermemory's licensing, deployment, and research details are drawn from its public materials. We give its local deployment and published results their due while explaining where the metrics cannot be compared directly.

Frequently Asked Questions

Is Supermemory open source and free to self-host, and how does it compare with EverOS?

Both EverMind and Supermemory permit self-hosting. Supermemory publishes MIT-licensed SDK/components, but its self-hosted server is not open source and is free only within a lite license limit. EverOS is Apache 2.0 licensed. EverMind's storage stack runs on local Markdown, SQLite, and LanceDB. No MongoDB, Elasticsearch, or Redis dependency is required, which reduces infrastructure overhead for teams running their own instances.

Which memory layer has better retrieval accuracy and latency for production AI agents?

EverMind reports LoCoMo, LongMemEval answer accuracy, HaluMem recall, and p95 latency for its setup. Supermemory publishes LongMemEval-S Recall@20 and other results. These answer different questions; run both on the same dataset and scoring method to compare quality and speed.

Does EverMind or Supermemory offer stronger MCP and SDK support for agents like Claude Code, Codex, and Cursor?

Both vendors offer SDK routes and hosted-platform MCP options; Supermemory local does not include its MCP server. Check the specific client versions and hosted-versus-local setup required for Claude Code, Codex, and Cursor rather than assuming one product lacks MCP support.

How does EverMind compare to Supermemory versus Mem0?

A dedicated article covers retrieval architecture, pricing, and SDK depth for the EverMind vs Mem0 comparison. This page focuses strictly on EverMind and Supermemory.

Can I migrate my existing memory data between EverMind and Supermemory without vendor lock-in?

EverOS files can be copied and inspected directly in a self-hosted setup. Supermemory offers both hosted and local deployments, so export and migration steps depend on which path holds your data. Verify available API/export fields, then test retrieval after import.

Which is more cost-effective at scale, EverMind or Supermemory?

EverMind reports about 10× lower token cost than a full-context baseline for its setup. Supermemory's hosted plans have their own credits and usage limits, and its self-hosted option has infrastructure costs. A matched workload is needed to compare total cost.

What do Reddit and technical communities say about EverMind vs Supermemory, and where is the verifiable benchmark evidence?

Both EverMind and Supermemory publish memory-related results. EverMind reports LoCoMo, LongMemEval, and HaluMem; Supermemory reports LongMemEval-S Recall@20 and other evaluations. Their metrics and test setups differ, so community discussion is no substitute for a matched pilot.